AiThority Interview with Jessica Kipper, Senior Director, Software Product Management at Schneider Electric
Hi Jessica, please tell us a bit about your role at Schneider Electric and how you arrived at the company.
I lead the Software Product Management team within Schneider Electric’s Sustainability Business. My team is focused on maximizing the value of our technology investment for our clients and their stakeholders, digitizing service delivery and accelerating the energy and sustainability journey. I’ve served in various technology roles during my 20+ year tenure with the organization. In my role, I get to craft robust technology roadmaps alongside our clients and execute on these plans with cross-functional colleagues at Schneider Electric. My team and I recently led our latest technology advancement – the expansion of EcoStruxure Resource Advisor into ESG data management and reporting.
How does Schneider Electric drive next-gen automation in energy management? Could you highlight some of the technologies that are currently available for customers?
We combine our long-standing domain expertise with cutting-edge innovation in AI, machine learning, and deep learning to empower smarter decision-making, agility, and decarbonization. The power of AI helps businesses to optimize operations and achieve greater energy transparency. Across the entire Schneider Electric portfolio, AI solutions are deployed in the EcoStruxure™ platform to numerous markets including building, data centers, infrastructure, and industry.
You recently announced the EcoStruxure Resource Advisor for ESG module. Could you elaborate more on this development?
Resource Advisor is the starting place for any enterprise that is seriously interested in gaining global insight into their energy and sustainability performance— and taking proactive steps towards reducing their energy costs and decarbonizing their operations. Now, with the recent expansion of Resource Advisor into ESG data management and reporting, business leaders can manage their corporate ESG, sustainability, and energy data all in one place, making it simple to track and interpret data, report out, and identify areas of opportunity, strength, and improvement across an increasingly complex resource landscape. This evolution was done in partnership with our customers and Schneider Electric experts, designed to support the emerging demands around ESG data management and reporting on a global level. Connecting fragmented energy, sustainability and ESG data, collaborating with efficiency, reporting with clarity, and managing performance in one place just got easier with our latest development in Resource Advisor.
How do you leverage AI and machine learning for Resource Advisor? What are the tangible benefits of bringing in AI to the ESG scenario?
The application of AI and machine learning within our software and services drive innovation in the areas of data collection, data quality assurance, energy market forecasting and portfolio optimization to drive confident decision-making. When it comes to ESG reporting, this is just one piece of the complex puzzle on our clients’ total journey to strategize, digitize and decarbonize.
Our clients reap many tangible benefits from incorporating AI into our processes. We can reduce clients’ energy costs by predicting periods of peak energy costs and optimizing their energy hedges portfolio. We simplify data entry through advanced document practices. Additionally, we can optimize our clients’ paths towards net-zero by selecting decarbonization projects, which fit their unique needs and constraints.
The accuracy of our data is critical to every downstream process in our business. When looking to source energy on behalf of our clients, we need to have an accurate representation of their consumption patterns. When a client seeks to report their C02 emissions, we need to have confidence in the values we report. For this reason, we work hard to identify outliers in our system, and these data quality services are underpinned by AI and machine learning. When we think about decarbonization, it’s important to understand the drivers of our clients’ consumption. Our efficiency recommendations are based on assessments of building performance relative to production, occupancy, weather, and other drivers.
As the field of machine learning continues to progress, we expect to see more applications in which intelligent large-language models are used to gather information from client documents and other materials, to ensure a robust accounting of all ESG metrics.
Please tell us more about the various global ESG frameworks and how AI has helped customers win the ESG narrative.
Resource Advisor contains a built-in global framework library that includes the leading ESG frameworks (GRI, SASB, CDP, TCFD, and UN SDGs), and enables clients to align specific indicators to relevant frameworks directly in the platform, simplifying the reporting process. As we continue to grow this global library, AI will provide key recommendations of where and what to disclose and benchmark with peers, driving further efficiencies. Increasingly, global legislative items are becoming even more crucial to clients, stemming from growing pressure from investors (both voluntary and mandatory) to serve as responsible stewards in the sustainability landscape. Efficient ESG reporting is therefore increasingly top of mind, supporting their data and reporting needs. AI can also play a role in guiding customers to be even more efficient in their total ESG reporting process, in response to these evolving market trends. As the market continues to mature, we aim to support our clients on their mission to tell powerful ESG stories, and to empower them to utilize their ESG reporting as a strategic management tool across their business.
Your take on the future of AI and ML/ Big Data Science in Energy Management initiatives. What is your roadmap at Schneider Electric as an ESG partner?
It’s hard to identify two things in the world which are changing as rapidly as the energy landscape and the capabilities of artificial intelligence. In the energy world, we’re experiencing a major shift from carbon-intense, centralized electricity generation to distributed, smaller-scale renewable sources. We are seeing the electrification of the transportation industry, a focus on automation in the home and the workplace, and a reimagination of the electric grid. The Internet of Things (IoT) and AI are enabling energy optimization to occur at a variety of levels – from the grid down to individual connected devices. Balancing generation from many disparate renewable sources, energy storage at a grid-level and grid load poses an exciting challenge for machine learning, big data, and AI.
At Schneider Electric, we focus on “collaborative intelligence” – pairing the skills of our highly experienced experts with digital tools capable of yielding recommendations or generating forecasts in milliseconds to drive value. We use machine learning to identify billing errors, meter-read issues, or data capture errors to ensure high quality data for our clients. We use machine learning to forecast grid-level pricing events which might impact the price our clients pay for energy. We use advanced optimization methods paired with industry expertise to identify decarbonization pathways, which fit the unique needs of our clients. We utilize natural language processing and computer vision to understand client energy invoices, parse energy contracts, identify green energy opportunities, and more. ESG tracking and consultation is just one critical component of what we do here at Schneider, and you can expect us to continue innovating around all of our services and software, adding AI where it adds value and streamlines our clients’ experience.
At Schneider Electric, we are excited to harness data-led technologies and support clients on their ambitious energy, sustainability and ESG journeys. Our pursuit of a sustainable, decarbonized future will continue to be driven by digitization, electrification and the intentional activation of AI, machine learning, and other cutting-edge technologies.
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